About the Program
The Bioinformatics and Computational Biology Unit (BCBU) conducts research at the interface of maternal-fetal medicine, genomics, mathematical modeling, machine learning and statistics. The Unit collaborates with others, including Maternal Fetal Immunobiology, Single Cell Genomics, and Fetal and Maternal Imaging, in clinical and translational research aimed at developing early biomarkers of obstetrical disease and elucidating the mechanisms of disease. A key focus of this unit is the development of software tools that can be used by researchers and clinicians to assess fetal growth and the risk of obstetrical disease. The BCBU has also an important role in training post-graduate and graduate-level professionals, and supports the Maternal-Fetal Medicine fellows in the area of experimental design and statistical analysis.
Objectives
- Identify molecular biomarkers and develop risk prediction models and software to predict obstetrical disease such as preeclampsia, fetal death and spontaneous preterm birth
- Implement personalized assessment of imaging-based biomarkers for the prediction of fetal growth restriction and spontaneous preterm birth
- Develop novel data analysis techniques and software tools for analysis of genomics data
- Develop machine learning methods for genomics data and use crowdsourcing to evaluate the accuracy of omics-based prediction models
Research Highlights
- Organized the DREAM Preterm Birth Prediction Challenge and identified maternal blood transcriptomic signatures for pregnancy dating and assessment of the risk of spontaneous preterm birth
- Described maternal blood proteomic and transcriptomic changes during normal pregnancy
- Presented amniotic fluid cell-free RNA signatures to assess fetal development during normal pregnancy
- Identified maternal blood proteomic changes that predict future onset of preeclampsia and fetal death
- Created a customized standard for fetal weight and demonstrated improved detection of fetuses at risk of perinatal death
- Developed and assessed methods for gene setand pathway analysis with omics data in the top tier of the most frequently used Bioconductor software packages
- Developed award-winning pipelines to build molecular classifiers from bulk and single-cell genomics data
Select Publications
- Amniotic fluid cell-free transcriptome: a glimpse into fetal development and placental cellular dynamics during normal pregnancy. Tarca AL, Romero R, Pique-Regi R, Pacora P, Done B, Kacerovsky M, Bhatti G, Jaiman S, Hassan SS, Hsu CD, Gomez-Lopez N. BMC Med Genomics;13(1):25, 2020. PMID: 32050959
- Maternal whole blood mRNA signatures identify women at risk of early preeclampsia: a longitudinal study. Tarca AL, Romero R, Erez O, Gudicha DW, Than NG, Benshalom-Tirosh N, Pacora P, Hsu CD, Chaiworapongsa T, Hassan SS, Gomez-Lopez N. J Matern Fetal Neonatal Med:1-12, 2020. PMID: 31900005
- Fetal growth percentile software: a tool to calculate estimated fetal weight percentiles for 6 standards. Bhatti G, Romero R, Cherukuri K, Gudicha DW, Yeo L, Kavdia M, Tarca AL. Am J Obstet Gynecol;222(6):625-8, 2020. PMID: 32067969
- Targeted expression profiling by RNA-Seq improves detection of cellular dynamics during pregnancy and identifies a role for T cells in term parturition. Tarca AL, Romero R, Xu Z, Gomez-Lopez N, Erez O, Hsu CD, Hassan SS, Carey VJ. Sci Rep; Sci Rep. 2019 Jan 29;9(1):848. PMID: 30696862
- A new customized fetal growth standard for African American women: the PRB/NICHD Detroit study. Tarca AL, Romero R, Gudicha DW, Erez O, Hernandez-Andrade E, Yeo L, Bhatti G, Pacora P, Maymon E, Hassan SS. Am J Obstet Gynecol; 218(2S):S679-S91 e4, 2018. PMID: 29422207
Faculty
